Summary of Non-stationary Domain Generalization: Theory and Algorithm, by Thai-hoang Pham et al.
Non-stationary Domain Generalization: Theory and Algorithm
by Thai-Hoang Pham, Xueru Zhang, Ping Zhang
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to domain generalization in non-stationary environments, where domains evolve along a specific direction. It examines the impact of environmental non-stationarity on model performance and establishes theoretical upper bounds for model error at target domains. The authors then introduce an adaptive invariant representation learning algorithm that leverages this non-stationary pattern to train models that perform well on unseen target domains. This approach is evaluated on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning. Currently, models are not very good at dealing with new, unexpected situations. They’re trained on specific data sets and can’t generalize as well when the situation changes or they encounter something completely new. The authors propose a way to make models more robust to these kinds of changes by incorporating patterns that might emerge in the future. This is important because many real-world applications involve changing conditions, such as time or location. |
Keywords
» Artificial intelligence » Domain generalization » Machine learning » Representation learning
